Advances in AI/ML are seen critical to advance our understanding of the disease and to bring better and more efficacious treatments to patients. As part of the drug development life-cycle vast amounts of clinical trials data are collected in order to identify targets of interest, discover biomarkers to stratify patients who could benefit from the drug, and to study the safety and benefit profile of the drug. Furthermore, after the drug is brought to the market its use in broader population is collected in a wide range of real-world data sources including, but not limited to, electronic medical records, disease registries, health insurance claims​, and digital devices​. Thus far the Pharma industry has not leveraged on the wealth of this information to deliver truly personalized care for patients.

Development of advanced ​statistical and machine learning​ methodologies combined with the availability of scalable computing environments is fueling a new wave of digitization in Pharma R&D pipelines thereby creating possibilities to discover and develop personalized medicines. This track would invite experts from industry and academia to share their experiences in using AI/ML for Pharma R&D.